Hybrid fireworks explosion optimization algorithm using elite opposition-based learning

doi:10.11772/j.issn.1001-9081.2014.10.2886

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (10): 2886-2890.DOI: 10.11772/j.issn.1001-9081.2014.10.2886

• Artificial intelligence • Previous Articles Next Articles

Hybrid fireworks explosion optimization algorithm using elite opposition-based learning

WANG Peichong¹,²,GAO Wenchao²,³,QIAN Xu²,GOU Haiyan⁴,WANG Shenwen¹

1. School of Information Engineering, Shijiazhuang University of Economics, Shijiazhuang Hebei 050031, China;
2. School of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China;
3. Department of Computer, Tsinghua University, Beijing 100084, China;
4. HuaXin College, Shijiazhuang University of Economics, Shijiazhuang Hebei 050071, China

Received:2014-05-04 Revised:2014-06-16 Online:2014-10-30 Published:2014-10-01
Contact: WANG Peichong

应用精英反向学习的混合烟花爆炸优化算法

王培崇¹,²,高文超²,³,钱旭²,苟海燕⁴,汪慎文⁵

1. 石家庄经济学院信息工程学院，石家庄 050031；
2. 中国矿业大学(北京) 机电与信息工程学院，北京 100083；
3. 清华大学计算机系，北京 100084；
4. 石家庄经济学院华信学院，石家庄 050071
5. 石家庄经济学院信息工程学院，石家庄 050031

通讯作者: 王培崇
作者简介:王培崇（1972-），男，河北辛集人，副教授，博士，主要研究方向：计算智能、机器学习；
高文超（1986-），女，山东泰安人，讲师，博士，主要研究方向：模式识别；
钱旭（1962-），男，江苏南京人，教授，博士生导师，博士，主要研究方向：信息融合；
苟海燕（1984-），女，陕西西安人，硕士，主要研究方向：计算智能；
汪慎文（1978-），男，湖北武汉人，副教授，博士，主要研究方向：计算智能。
基金资助:
教育部博士点建设基金资助项目;河北省科技支撑计划项目;河北省教育厅基金资助项目;石家庄经济学院基金预研项目;博士科研基金资助项目;河北省青年拔尖人才支持计划项目

Abstract

Abstract:

Concerning the problem that Fireworks Explosion Optimization (FEO) algorithm is easy to be premature and has low solution precision, an elite Opposition-Based Learning (OBL) was proposed. In every iteration, OBL was executed by the current best individual to generate an opposition search populations in its dynamic search boundaries, thus the search space of the algorithm was guided to approximate the optimum space. This mechanism is helpful to improve the balance and exploring ability of the FEO. For keeping the diversity of population, the sudden jump probability of the individual to the current best individual was calculated, and based on it, the roulette mechanism was adopted to choose the individual which entered into the child population. The experimental simulation on five classical benchmark functions show that, compared with the related algorithm, the improved algorithm has higher convergence rate and accuracy for numerical optimization, and it is suitable to solve the high dimensional optimization problem.

摘要：

针对烟花爆炸优化(FEO)算法容易早熟、解精度低的弱点，提出了一种精英反向学习(OBL)的解空间搜索策略。在每次迭代过程中均对当前最佳个体执行反向学习，生成其动态搜索边界内的反向搜索种群，引导算法向包含全局最优的解空间逼近，以提高算法的平衡和探索能力。为了保持种群的多样性，计算种群内个体对当前最佳个体的突跳概率，并依据此概率值采用轮盘赌机制选择进入子种群的个体。通过在5组标准测试函数的实验仿真并与相关的算法对比，结果表明所提出的改进算法对数值优化具有更高的收敛速度和收敛精度，适合求解高维的数值优化问题。

CLC Number:

TP18

WANG Peichong GAO Wenchao QIAN Xu GOU Haiyan WANG Shenwen. Hybrid fireworks explosion optimization algorithm using elite opposition-based learning[J]. Journal of Computer Applications, 2014, 34(10): 2886-2890.

王培崇高文超钱旭苟海燕汪慎文. 应用精英反向学习的混合烟花爆炸优化算法[J]. 计算机应用, 2014, 34(10): 2886-2890.

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Hybrid fireworks explosion optimization algorithm using elite opposition-based learning

应用精英反向学习的混合烟花爆炸优化算法

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